Model comparison
Grok 4.6 vs Qwen3 14B
Grok 4.6 is the stronger model overall, scoring 56.9 to 35.5 on the Noometry Index. Qwen3 14B costs 4.9× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 8 shared benchmarks.
Alibaba (Qwen)Summary
- They share 8 benchmarks with published results for both. Grok 4.6 scores higher in 6 categories and Qwen3 14B in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.6 leads 61.4 to 18.5.
- The biggest single-benchmark swing is Chess Puzzles: 40% for Grok 4.6 and 4% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 131K.
- Qwen3 14B has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Qwen3 14B | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 56.9 | 35.5 |
| Released | 2026-08-12 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 500K | 131K |
| Max output | 500K | 8K |
| Input $ / M tokens | $2 | $0.35 |
| Output $ / M tokens | $6 | $1.40 |
| Results tracked | 49 | 12 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.5 (#16), Qwen3 14B: 37.3 (#195)
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| SciCode | 56.5% | 31.6% |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1617 | — |
| FrontierSWE | 25.3% | — |
| WeirdML | 67.3% | — |
| LMArena Coding | 1465 | — |
| ALE-Bench | 1,508 | — |
Agentic & Tool Use Grok 4.6 leads
Grok 4.6: 39.4 (#27), Qwen3 14B: 29.6 (#83)
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| APEX-Agents | 65.3% | — |
| Berkeley Function Calling Leaderboard | — | 41% |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.4 (#20), Qwen3 14B: 18.5 (#280)
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| CritPt | 19.7% | 0% |
| Chess Puzzles | 40% | 4% |
| DTBench | 97.3% | 64% |
| LMCA | 48.5% | 18.2% |
| Epoch Capabilities Index | 156.44 | 138.23 |
| ARC-AGI-2 | 67.1% | — |
| SimpleBench | 75.9% | — |
| Kagi LLM Benchmark | — | 49.1% |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| EBR-Bench | 30.5% | — |
| LMArena Hard Prompts | 1447 | — |
| Mystery Game Puzzles | 34% | — |
Math Grok 4.6 leads
Grok 4.6: 67.0 (#24), Qwen3 14B: 38.6 (#133)
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.2% | 66.4% |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| ProofBench | 51% | — |
| LMArena Math | 1423 | — |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#20), Qwen3 14B: 39.3 (#134)
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 94% | 63.8% |
| SimpleQA Verified | 49.3% | — |
| Vectara Hallucination Rate | — | 5.4% |
| LMArena Expert | 1467 | — |
Multimodal Not comparable
Grok 4.6: 43.6 (#23), Qwen3 14B: —
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| LMArena Vision | 1263 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Not comparable
Grok 4.6: 53.0 (#74), Qwen3 14B: —
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1420 | — |
| LMArena Chinese | 1480 | — |
| LMArena French | 1461 | — |
| LMArena German | 1431 | — |
| LMArena Japanese | 1376 | — |
| LMArena Korean | 1397 | — |
| LMArena Russian | 1422 | — |
| LMArena Spanish | 1404 | — |
Instruction Following Not comparable
Grok 4.6: 75.4 (#63), Qwen3 14B: —
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1431 | — |
Long Context Grok 4.6 leads
Grok 4.6: 44.5 (#66), Qwen3 14B: 38.1 (#204)
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| LMArena Longer Query | 1454 | — |
Writing & Preference Not comparable
Grok 4.6: 62.3 (#80), Qwen3 14B: —
| Benchmark | Grok 4.6 | Qwen3 14B |
|---|---|---|
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1428 | — |
| LMArena Multi-Turn | 1425 | — |
Frequently asked questions
Is Grok 4.6 better than Qwen3 14B?
Grok 4.6 is the stronger model overall, scoring 56.9 to 35.5 on the Noometry Index. Qwen3 14B costs 4.9× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Which is cheaper, Grok 4.6 or Qwen3 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Qwen3 14B better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.5 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 131K.
How many benchmarks do Grok 4.6 and Qwen3 14B share?
8 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Qwen3 14B has 12.